DESCRIPTION (Verbatim from the Applicant's Abstract): The applicants will develop statistical SURFACE fractal dimension (S-fd) features, which will discriminate benign from malignant breast masses on MRI and mammographic images. S-fd features derived from three functional representations of breast mass image data will be evaluated: (1) signal intensity of mass on single MRI slice; (2) mammographic density of mass on digitized mammogram; (3) thickness of mass, computed from 3-dim MRI data. The S-fd features are statistics from Fractal Interpolation Function Models (FIFM) of breast mass image data. In prior research, FIFM BORDER fd (B-fd) features were shown to provide more robust discrimination in data-limited applications such as breast mass analysis than other fd algorithms. FIFM SURFACE fractals represent multiresolution differences between benign and malignant masses more accurately and more extensively than FIFM BORDER fractals, and therefore may provide more reliable discriminatory information. Robust S-fd features, which discriminate benign from malignant masses, will have application in computer-aided-diagnosis systems under development. PROPOSED COMMERCIAL APPLICATION: The new features will have significant value to the diagnostician who must distinguish benign from malignant breast lesions. The algorithm is readily integrated into CAD systems and has potential utility for a variety of medical and industrial applications in texture analysis of data-limited surfaces.